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bmf - efficient Bayesian mail filter


bmf [-t] [-n] [-s] [-N] [-S] [-f fmt] [-d db] [-i file] [-k n] [-m type] [-p]
[-v] [-V] [-h]


bmf is a Bayesian mail filter. In its normal mode of operation, it takes an email message
or other text on standard input, does a statistical check against lists of "good" and
"spam" words, registers the new data, and returns a status code indicating whether or not
the message is spam. BMF is written with fast, zero-copy algorithms, coded directly in C,
and tuned for speed. It aims to be faster, smaller, and more versatile than similar

bmf supports both mbox and maildir mail storage formats. It will automatically process
multiple messages within an mbox file separately.


Without command-line options, bmf processes the input, registers it as either "good" or
"spam", and returns the appropriate error code. The wordlist directory and nonexistent
wordfiles are created if absent.

-t Test to see if the input is spam. The word lists are not updated. A report is written
to stdout showing the final score and the tokens with the highest deviation form a mean of

-n Register the input as non-spam.

-s Register the input as spam.

-N Register the input as non-spam and undo a prior registration as spam.

-S Register the input as spam and undo a prior registration as non-spam.

-f fmt Specify database format. Valid formats are text, db, and mysql. Text is always
valid. The others may not be available if the corresponding option was not enabled at
compile time. The default is db if available, else text.

-d db Specify database or directory for loading and saving word lists. The default is
~/.bmf in text mode.

-i file Use file for input instead of stdin.

-k n Specify the number of extrema (keepers) to use in the Bayes calculation. The default
is 15.

-m fmt Specify mail storage format. Valid formats are mbox and maildir. The default is to
automatically detect the mail storage format. This option is deprecated.

-p Copy the input to the output (passthrough) and insert spam headers in the style of
SpamAssassin. An X-Spam-Status header is always inserted with processing details. The
contents of this header always begin with either "Yes" or "No". If the input is judged to
be spam, the header "X-Spam-Flag: YES" is also inserted.

-v Be more verbose. This option is not well supported yet.

-V Display version information.

-h Display usage information.


bmf treats its input as a bag of tokens. Each token is checked against "good" and "bad"
wordlists, which maintain counts of the numbers of times it has occurred in non-spam and
spam mails. These numbers are used to compute the probability that a mail in which the
token occurs is spam. After probabilities for all input tokens have been computed, a fixed
number of the probabilities that deviate furthest from average are combined using Bayes's
theorem on conditional probabilities.

While this method sounds crude compared to the more usual pattern-matching approach, it
turns out to be extremely effective. Paul Graham's paper A Plan For Spam:
http://www.paulgraham.com/spam.html is recommended reading.

bmf improves on Paul's proposal by doing smarter lexical analysis. In particular,
hostnames and IP addresses are not discarded, and certain types of MTA information are
discarded (such as message ids and dates).

MIME and other attachments are not decoded. Experience from watching the token streams
suggests that spam with enclosures invariably gives itself away through cues in the
headers and non-enclosure parts. Nonetheless, I would like to add the ability to decode
quoted-printable and perhaps base64 encodings for textual attachments.


Please see the /usr/share/doc/bmf/README.gz for samples and suggestions.


In passthrough mode: zero for success, nonzero for failure.

In non-passthrough mode: 0 for spam; 1 for non-spam; 2 for I/O or other errors.

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